Reference : VoIP Traffic Modelling using Gaussian Mixture Models, Gaussian Processes and Interact...
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/23637
VoIP Traffic Modelling using Gaussian Mixture Models, Gaussian Processes and Interactive Particle Algorithms
English
Simionovici, Ana-Maria mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Tantar, Alexandru mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Tchernykh, Andrei mailto [CICESE Research Center, Ensenada, Baja California, Mexico]
Cortes-Mendoza, Jorge Mario mailto [CICESE Research Center, Ensenada, Baja California, Mexico]
Didelot, Loic mailto [MIXvoip S.a. , Luxembourg]
5-Dec-2015
6
Yes
International
Globecom
from 5-12-2015 to 10-12-2015
IEEE
San Diego
USA
[en] The paper deals with an important problem in the
Voice over IP (VoIP) domain, namely being able to understand
and predict the structure of traffic over some given period of time.
VoIP traffic has a time variant structure, e.g. due to sudden peaks,
daily or weekly moving patterns of activities, which in turn makes
prediction difficult. Obtaining insights about the structure and
trends of traffic has important implications when dealing with
the nowadays cloud-deployed VoIP services. Prediction techniques
are applied to anticipate the incoming traffic, for an efficient
distribution of the traffic in the system and allocation of resources.
The article looks in a critical manner at a series of machine
learning techniques. We namely compare and review (using real
VoIP data) the results obtained when using a Gaussian Mixture
Model (GMM), Gaussian Processes (GP), and an evolutionary like
Interacting Particle Systems based (sampling) algorithm. The
experiments consider different setups as to verify the time variant
traffic assumption.
Fonds National de la Recherche - FnR ; CONACYT (Consejo Nacional de Ciencia y Tecnologa, Mexico)
http://hdl.handle.net/10993/23637
FnR

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